Using the Maximum Entropy Principle to Combine Simulations and Solution Experiments

TL;DR

This paper reviews methods that integrate molecular dynamics simulations with experimental data using the maximum entropy principle, focusing on Lagrangian multiplier techniques and addressing error modeling in experimental data.

Contribution

It provides a comprehensive overview of combining MD simulations and experiments via maximum entropy, highlighting methods, error handling, and practical challenges.

Findings

Reweighting and on-the-fly optimization methods are effective for data integration.

Modeling experimental errors is crucial for accurate data combination.

Simple models illustrate typical difficulties in applying these methods.

Abstract

Molecular dynamics (MD) simulations allow investigating the structural dynamics of biomolecular systems with unrivaled time and space resolution. However, in order to compensate for the inaccuracies of the utilized empirical force fields, it is becoming common to integrate MD simulations with experimental data obtained from ensemble measurements. We here review the approaches that can be used to combine MD and experiment under the guidance of the maximum entropy principle. We mostly focus on methods based on Lagrangian multipliers, either implemented as reweighting of existing simulations or through an on-the-fly optimization. We discuss how errors in the experimental data can be modeled and accounted for. Finally, we use simple model systems to illustrate the typical difficulties arising when applying these methods.